We trained the individual models from scratch (except the efficientnet which is too costly to train from scratch) to prevent any pretraining bias to affect our evaluation results. We run the baselines according to the hyperparameter configurations used in their original papers,
but due to some absence of implementation details, on some particular datasets/networks the settings may be slightly different.
All the data augmentation techniques for a given architecture and dataset were run for a fixed number of epochs which were enough for the models to converge onto a test set accuracy, considering our prime objective was to test other data augmentation techniques against ours rather than matching state-of-the-art results. All the models were implemented in Pytorch [1]} framework and all data augmentation technique implementation was also done in requirements with the Pytorch framework.
